PA054 Formal Methods in Systems Biology

Faculty of Informatics
Spring 2013
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
doc. RNDr. David Šafránek, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Fri 14:00–15:50 B411
Prerequisites
The course requires elementary knowledge of formal techniques achieved at bachelor level. This is an interdisciplinary course. The course is recommended especially for students of Bioinformatics. The course is also suitable for students of all other applied and theoretical study branches, namely Parallel and Distributed Systems and Theoretical Computer Science.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 19 fields of study the course is directly associated with, display
Course objectives
At the end of this course students should be able to: understand the actual trends and open problems in the domain of formal methods for complex systems modeling; apply formal methods in the modern biological research in systems biology; employ relevant software tools to solve particular problems in modeling and simulation of biological systems
Syllabus
  • Problem definition: Modeling and analysis in systems biology. Motivation for application of formal methods.
  • Overview of formal methods employed for analysis of biological hypotheses. Model specification. Model size and state explosion problem.
  • Modeling and simulation: Deterministic vs. non-deterministic models. Continuous vs. discrete models. Models with parameter uncertainty. Approximation and abstraction. Simulation and analysis.
  • Qualitative models: Boolean networks, Petri nets.
  • Quantitative models: Timed Boolean networks, Markov chains, Stochastic Petri nets, relations to continuous and hybrid models.
  • Formal specification of models: Kappa-calculus, Stochastic Petri nets, Stochastic Pi-Calculus and related formalisms.
  • Model checking: Application in the process of model validation. Properties of in silico models vs. in vivo/in vitro experiments. Model checking tools for biological models.
  • Models with parameter uncertainty: Parameter estimation. Robustness analysis.
Literature
    recommended literature
  • Formal methods for computational systems biology : 8th International School on Formal Methods for the Design of Computer, Communication and Software Systems, SFM 2008 : Bertinoro, Italy, June 2-7, 2008 : advanced lectures. Edited by Marco Bernardo - Pierpaolo Degano - Gianluigi Zavattaro. Berlin: Springer, 2008, x, 523. ISBN 9783540688921. info
  • Computational modeling of genetic and biochemical networks. Edited by James M. Bower - Hamid Bolouri. Cambridge: Bradford Book, 2001, xx, 336. ISBN 0262524236. info
    not specified
  • VRIES, Gerda de. A course in mathematical biology : quantitative modeling with mathematical and computational methods. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 2006, xii, 309. ISBN 0898716128. URL info
  • ALON, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/Crc, 2006. info
  • PALSSON, Bernhard. Systems biology : properties of reconstructed networks. 1st pub. Cambridge [England]: Cambridge University Press, 2006, xii, 322. ISBN 9780521859035. info
Teaching methods
Lectures, group projects. Optional homeworks.
Assessment methods
oral exam (40%), semester project (60%)
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2013, recent)
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